Install OEP SDKs#

This page covers installing the OEP Vision AI SDK on a provisioned Handheld (Soldier System) target and validating the stack with a sample object detection pipeline.

The provisioned image ships with system-level dependencies only — kernel, GPU/NPU drivers, Docker Engine, and container device plugins. The OEP Vision AI SDK, DL Streamer container images, sample models, and OpenVINO Python runtime must be installed on the target as described below.

For image build and platform provisioning, see Infrastructure Setup.

Prerequisites#

  • Handheld platform provisioned per Infrastructure Setup.

  • Passwordless SSH or console access to the target.

  • Internet connectivity (or configured proxy) on the target for package and container image downloads.

  • Minimum 20 GB free disk space for SDK content, images, and sample models.

Step 1: Verify Hardware Accelerators#

Confirm the GPU and NPU are visible to the OS before installing the SDK:

# GPU (integrated Arc, exposed as DRI render device)
ls -l /dev/dri/

# NPU (exposed via intel_vpu driver)
ls -l /dev/accel/
lsmod | grep intel_vpu

Expected: card0/renderD128 under /dev/dri, accel0 under /dev/accel, and the intel_vpu module loaded.

Step 2: Install the OEP Vision AI SDK#

Run the official OEP Vision AI SDK installer on the target. It configures Docker, pulls the DL Streamer image, and installs the OpenVINO tooling and sample content:

curl https://raw.githubusercontent.com/open-edge-platform/edge-ai-suites/refs/heads/main/metro-ai-suite/metro-sdk-manager/scripts/oep-vision-ai-sdk.sh | bash

The installer sets up:

  • Docker containerization platform (verified/configured)

  • DL Streamer video analytics framework (container image)

  • OpenVINO inference optimization toolkit

  • Pre-trained model repositories and sample implementations

For full details, see the OEP Vision AI SDK Get Started guide.

Step 3: Run the Sample Object Detection Pipeline#

Validate the installation with the SDK’s sample detection workflow.

Create a working directory:

mkdir -p ~/oep/oep-vision-get-started-tutorial
cd ~/oep/oep-vision-get-started-tutorial

Download the sample video and model:

wget -O sample.mp4 \
  https://github.com/intel-iot-devkit/sample-videos/raw/master/person-bicycle-car-detection.mp4

mkdir -p models/intel/pedestrian-and-vehicle-detector-adas-0001/FP32/

wget -O "models/intel/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.xml" \
  "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.xml?raw=true"

wget -O "models/intel/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.bin" \
  "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.bin?raw=true"

wget -O "models/intel/pedestrian-and-vehicle-detector-adas-0001/pedestrian-and-vehicle-detector-adas-0001.json" \
  "https://raw.githubusercontent.com/open-edge-platform/dlstreamer/refs/heads/main/samples/gstreamer/model_proc/intel/pedestrian-and-vehicle-detector-adas-0001.json"

Start the DL Streamer container with display forwarding:

xhost +

docker run --rm -it --name dlstreamer \
  -v $PWD:/data \
  -e DISPLAY=$DISPLAY \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  intel/dlstreamer:2026.2.0-ubuntu24-rc2

Inside the container, run the detection pipeline:

gst-launch-1.0 filesrc location=/data/sample.mp4 ! \
  decodebin ! videoconvert ! \
  gvadetect model=/data/models/intel/pedestrian-and-vehicle-detector-adas-0001/FP32/pedestrian-and-vehicle-detector-adas-0001.xml \
    model-proc=/data/models/intel/pedestrian-and-vehicle-detector-adas-0001/pedestrian-and-vehicle-detector-adas-0001.json \
    device=CPU ! \
  gvawatermark ! videoconvert ! autovideosink

The video plays with detection boxes overlaid on pedestrians and vehicles. To target the GPU or NPU instead, change device=CPU to device=GPU or device=NPU (add --device intel.com/gpu=card0 and/or --device intel.com/npu=npu0 to the docker run command).

Exit the container with exit.

Next Steps#

Continue with the OEP Vision AI SDK tutorials to explore benchmarking, multi-stream processing, real-time detection, and profiling: